Technical Field
The present disclosure generally relates to techniques for ranking points of interest disposed within geographic regions of interest to a user. More particularly, and without limitation, the present disclosure relates to systems and methods for ranking points of interest based on, for example, input from a user, input from a social network associated with the user, and/or input from prior searches for maps and driving directions.
Background Information
Today, many sources of information exist to aid the general public in evaluating points of interest (POIs), such as restaurants, retailers, and various other attractions. For example, the general public may draw upon user-generated or editorially-generated ratings and reviews of the POIs to gain information on the quality of the POIs. However, such ratings and reviews are often limited in their ability to provide accurate and location-specific information on the POIs.
For example, a large number of ratings and/or reviews of a single POI may be needed to obtain a statistically-significant sample. However, characteristically low levels of user participation in the review process can render a statistically-significant sample difficult to obtain. Furthermore, ratings and reviews may be skewed towards an extremist bias, which renders these ratings and reviews effectively useless to the general public. Moreover, such ratings and reviews are often insufficiently local, as they pay little attention to a relative value of one POI as opposed to another POI within a local context of choice.
In addition to the above techniques, the public may also obtain information on the quality of POIs from one or more user-generated or editorially-generated leaderboard-style “Best Of” rankings of POIs. These rankings, however, are also generally of limited use to the consuming public. For example, these generated rankings often range over too wide a population of POIs, and thus obscure POIs that may be of great quality in a relatively small niche area. In addition, while the quality of POIs may change on a daily basis, rankings or “Best Of” lists require significant human capital and are often generated far too infrequently to reflect the currently quality of the POIs.
Furthermore, although systems may determine a rank of a POI based on a population of users that vote for or against the POI, such systems are rarely effective at incentivizing an individual user to add his or her vote. Additionally, these systems are often inadequate in creating a viral feedback-loop among the voters' social group to join in and vote on corresponding POIs.
In view of the foregoing, there is a need for improved systems and methods for ranking POIs. In addition, there is a need for improved systems and methods that can automatically and reliably rank POIs, including local POIs. Still further, there is a need for improved techniques for ranking POIs based on input, including input from a user, viral input from a user's social network, and/or additional input based on searches for maps and travel directions for traversing a route. Such systems and methods may be implemented in computer-based environments, such as the Internet and network environments that provide, for example, online content or functions (e.g., mapping, search, etc) to users.